Many organizations rely upon data replication to improve the reliability, fault-tolerance, and/or accessibility of their applications and/or data. Data replication typically involves replicating (e.g., in a passive or active manner) data from a primary site or device (such as an application server) to a secondary (i.e., backup) site or device (also known as a “replication target”).
Due to the high volumes of data generated during data replication, providers of data-replication services have long sought to maximize data storage performance while minimizing the cost of storage. Because of this, some providers have turned to thin-provisioning solutions in an effort to efficiently utilize available storage space. Thin-provisioning solutions typically allocate storage space from a common pool to computing systems on an as-needed or just-in-time basis in an effort to prevent storage space from going to waste.
Unfortunately, typical file or block-level replication techniques may prevent replication targets from identifying free or unused portions within data replicated from a primary site (such as an application server). For example, a replication target may be unable to interpret application or file-system data received from a primary site without access to the various application and/or file-system APIs used by the primary site when generating the data. Without this knowledge, the replication target may be unable to effectively utilize a thin-provisioned storage system for storing the replicated data since the replication target will be unable to identify (and subsequently instruct the thin-provisioned storage system to reclaim) free or unused storage space within the replicated data stored on the thin-provisioned storage system.